Stimulus placement system using subject neuro-response measurements

ABSTRACT

Example methods, apparatus, systems and machine readable media are disclosed herein for selecting advertisement or entertainment location based on neuro-response data. An example method includes analyzing first neuro-response data from a first subject exposed to source material. The example method also includes identifying a candidate location in the source material for introduction of an advertisement or entertainment based on the first neuro-response data. In addition, the example method includes analyzing second neuro-response data from at least one of the first subject and a second subject exposed to a combination of the source material and the advertisement or entertainment inserted in the candidate location. The example also includes determining an effectiveness of the advertisement or entertainment based on the second neuro-response data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent arises as a continuation of U.S. application Ser. No.12/199,557, which was filed on Aug. 27, 2008, and claims the benefitunder 35 U.S.C. §119(e) to U.S. Provisional Application 60/968,558,which was filed on Aug. 28, 2007, both of which are hereby incorporatedherein by reference in their entireties.

FIELD OF THE DISCLOSURE

The present disclosure relates to placing stimulus material usingsubject neuro-response measurements.

BACKGROUND

Conventional systems for placing stimulus material such as a media clip,product, brand image, offering, etc., are limited. Some placementsystems are based on demographic information, statistical data, andsurvey based response collection. However, conventional systems aresubject to semantic, syntactic, metaphorical, cultural, and interpretiveerrors.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, whichillustrate particular examples.

FIG. 1 illustrates one example of a system for selecting locations forstimulus material introduction.

FIG. 2 illustrates examples of stimulus attributes that can be includedin a stimulus attributes repository.

FIG. 3 illustrates examples of data models that can be used with astimulus and response repository.

FIG. 4 illustrates one example of a query that can be used with astimulus location selection system.

FIG. 5 illustrates one example of a report generated using the stimuluslocation selection system.

FIG. 6 illustrates one example of a technique for performing temporaland spatial location assessment.

FIG. 7 illustrates one example of technique for analyzing temporal andspatial location selection data.

FIG. 8 provides one example of a system that can be used to implementone or more mechanisms.

DETAILED DESCRIPTION

Reference will now be made in detail to some specific examples of thedisclosure including the best modes contemplated by the inventors forcarrying out the teachings of the disclosure. Examples of these specificexamples are illustrated in the accompanying drawings. While thedisclosure is described in conjunction with these specific examples, itwill be understood that it is not intended to limit the disclosure tothe described examples. On the contrary, it is intended to coveralternatives, modifications, and equivalents as may be included withinthe spirit and scope of the disclosure as defined by the appendedclaims.

For example, the techniques and mechanisms of the present disclosurewill be described in the context of particular types of data such ascentral nervous system, autonomic nervous system, and effector data.However, it should be noted that the techniques and mechanisms of thepresent disclosure apply to a variety of different types of data. Itshould be noted that various mechanisms and techniques can be applied toany type of stimuli. In the following description, numerous specificdetails are set forth in order to provide a thorough understanding ofthe present disclosure. Particular examples of the present disclosuremay be implemented without some or all of these specific details. Inother instances, well known process operations have not been describedin detail in order not to unnecessarily obscure the present disclosure.

Various techniques and mechanisms of the present disclosure willsometimes be described in singular form for clarity. However, it shouldbe noted that some examples include multiple iterations of a techniqueor multiple instantiations of a mechanism unless noted otherwise. Forexample, a system uses a processor in a variety of contexts. However, itwill be appreciated that a system can use multiple processors whileremaining within the scope of the present disclosure unless otherwisenoted. Furthermore, the techniques and mechanisms of the presentdisclosure will sometimes describe a connection between two entities. Itshould be noted that a connection between two entities does notnecessarily mean a direct, unimpeded connection, as a variety of otherentities may reside between the two entities. For example, a processormay be connected to memory, but it will be appreciated that a variety ofbridges and controllers may reside between the processor and memory.Consequently, a connection does not necessarily mean a direct, unimpededconnection unless otherwise noted.

Overview

Consequently, it is desirable to provide improved methods and apparatusfor selecting locations for introduction of stimulus materials.

A system evaluates and selects temporal and spatial locations forintroduction of stimulus material. Video streams, physical locations,print advertisements, store shelves, images, commercials, etc. areanalyzed to identify locations for introducing stimulus material, suchas messages, brand images, products, media, marketing and/or other salesmaterials. The system analyzes neuro-response measurements from subjectsexposed to stimulus material in different temporal and spatiallocations. Examples of neuro-response measurements includeElectroencephalography (EEG), Galvanic Skin Response (GSR),Electrocardiograms (EKG), Electrooculography (EOG), eye tracking, andfacial emotion encoding measurements. Neuro-response measurements areanalyzed to select temporal and spatial locations for introduction ofstimulus material.

EXAMPLES

Conventional placement systems such as product placement systems oftenrely on demographic information, statistical information, and surveybased response collection to determine optimal locations to placestimulus material, such as a new product, a brand image, a video clip,sound files, etc. One problem with conventional stimulus placementsystems is that conventional stimulus placement systems do notaccurately measure the responses to components of the experience. Theyare also prone to semantic, syntactic, metaphorical, cultural, andinterpretive errors thereby preventing the accurate and repeatableselection of stimulus placement locations.

Conventional systems do not use neuro-behavioral and neuro-physiologicalresponse blended manifestations in assessing the user response and donot elicit an individual customized neuro-physiological and/orneuro-behavioral response to the stimulus.

Conventional devices also fail to blend multiple datasets, and blendedmanifestations of multi-modal responses, across multiple datasets,individuals and modalities, to reveal and validate stimulus locationselection.

In these respects, the neuro-physiological and neuro-behavioral stimuluslocation selection system according to the present disclosuresubstantially departs from the conventional concepts and designs of theprior art, and in so doing provides an apparatus developed to provideneuro-physiological and neuro-behavioral response based measurements toselect stimulus locations. The selection mechanism may includemarketing, advertising and other audio/visual/tactile/olfactory stimulusincluding but not limited to communication, concept, experience,message, images, audio, pricing, and packaging.

The techniques and mechanisms of the present disclosure useneuro-response measurements such as central nervous system, autonomicnervous system, and effector measurements to improve stimulus locationselection. Some examples of central nervous system measurementmechanisms include Functional Magnetic Resonance Imaging (FMRI) andElectroencephalography (EEG). fMRI measures blood oxygenation in thebrain that correlates with increased neural activity. However, currentimplementations of fMRI have poor temporal resolution of few seconds.EEG measures electrical activity associated with post synaptic currentsoccurring in the milliseconds range. Subcranial EEG can measureelectrical activity with the most accuracy, as the bone and dermallayers weaken transmission of a wide range of frequencies. Nonetheless,surface EEG provides a wealth of electrophysiological information ifanalyzed properly. Even portable EEG with dry electrodes provide a largeamount of neuro-response information.

Autonomic nervous system measurement mechanisms include Galvanic SkinResponse (GSR), Electrocardiograms (EKG), pupillary dilation, etc.Effector measurement mechanisms include Electrooculography (EOG), eyetracking, facial emotion encoding, reaction time etc.

According to various examples, the techniques and mechanisms of thepresent disclosure intelligently blend multiple modes and manifestationsof precognitive neural signatures with cognitive neural signatures andpost cognitive neurophysiological manifestations to more accuratelyperform stimulus location selection. In some examples, autonomic nervoussystem measures are themselves used to validate central nervous systemmeasures. Effector and behavior responses are blended and combined withother measures. According to various examples, central nervous system,autonomic nervous system, and effector system measurements areaggregated into a measurement that allows assessment of theeffectiveness of the source material with stimulus material introducedat various locations.

In particular examples, subjects are exposed to stimulus material anddata such as central nervous system, autonomic nervous system, andeffector data is collected during exposure. According to variousexamples, data is collected in order to assess neuro-responsesignificance for source material with stimulus material introduced atvarious temporal and spatial locations. For example, a billboard may beevaluated for neuro-response significance before introducing anyadditional stimulus material. Areas of the billboard having lowneuro-response significance are then identified as candidate locationsfor introducing stimulus material. Stimulus material such as a brandimage is then introduced onto various versions the billboard atcandidate temporal and spatial locations and neuro-response significancefor each of the versions is evaluated. In other examples, lulls inneuro-response significance are identified as candidate locations. Instill other examples, still screens in a video or blank portions of animage are identified as candidate locations. Candidate locations mayalso be manually identified.

Many types of stimulus material may be placed into source material. Insome examples, source material is a physical display and stimulusmaterial is a newly packaged product for the display. In other examples,brand images are introduced into a video. Text advertisements may beplaced onto a web page or audio clips may be added to a music file. Anytype of stimulus material may be added to source material.

In particular examples, specific event related potential (ERP) analysesand/or event related power spectral perturbations (ERPSPs) are evaluatedfor different regions of the brain both before a subject is exposed tostimulus and each time after the subject is exposed to stimulus.

Pre-stimulus and post-stimulus differential as well as target anddistracter differential measurements of ERP time domain components atmultiple regions of the brain are determined (DERP). Event relatedtime-frequency analysis of the differential response to assess theattention, emotion and memory retention (DERPSPs) across multiplefrequency bands including but not limited to theta, alpha, beta, gammaand high gamma is performed. In particular examples, single trial and/oraveraged DERP and/or DERPSPs can be used to enhance selection ofstimulus locations.

A stimulus placement mechanism may incorporate relationship assessmentsusing brain regional coherence measures of segments of the stimulirelevant to the entity/relationship, segment effectiveness measuressynthesizing the attention, emotional engagement and memory retentionestimates based on the neuro-physiological measures includingtime-frequency analysis of EEG measurements, and differential saccaderelated neural signatures during segments where coupling/relationshippatterns are emerging in comparison to segments with non-coupledinteractions.

According to various examples, a stimulus location selection system caninclude automated systems with or without human intervention for theelicitation of potential object/individual groupings. For example, thesecould also include pattern recognition and object identificationtechniques. These sub-systems could include a hardware implementationand/or software implementations.

A variety of stimulus materials such as entertainment and marketingmaterials, media streams, billboards, print advertisements, textstreams, music, performances, sensory experiences, etc., can beanalyzed. According to various examples, enhanced neuro-response data isgenerated using a data analyzer that performs both intra-modalitymeasurement enhancements and cross-modality measurement enhancements.According to various examples, brain activity is measured not just todetermine the regions of activity, but to determine interactions andtypes of interactions between various regions. The techniques andmechanisms of the present disclosure recognize that interactions betweenneural regions support orchestrated and organized behavior. Attention,emotion, memory, and other abilities are not merely based on one part ofthe brain but instead rely on network interactions between brainregions.

The techniques and mechanisms of the present disclosure furtherrecognize that different frequency bands used for multi-regionalcommunication can be indicative of the effectiveness of stimuli. Inparticular examples, evaluations are calibrated to each subject andsynchronized across subjects. In particular examples, templates arecreated for subjects to create a baseline for measuring pre and poststimulus differentials. According to various examples, stimulusgenerators are intelligent and adaptively modify specific parameterssuch as exposure length and duration for each subject being analyzed.

A variety of modalities can be used including EEG, GSR, EKG, pupillarydilation, EOG, eye tracking, facial emotion encoding, reaction time,etc. Individual modalities such as EEG are enhanced by intelligentlyrecognizing neural region communication pathways. Cross modalityanalysis is enhanced using a synthesis and analytical blending ofcentral nervous system, autonomic nervous system, and effectorsignatures. Synthesis and analysis by mechanisms such as time and phaseshifting, correlating, and validating intra-modal determinations allowgeneration of a composite output characterizing the significance ofvarious data responses to effectively perform stimulus locationselection.

FIG. 1 illustrates one example of a system for performing stimulusplacement or stimulus location selection using central nervous system,autonomic nervous system, and/or effector measures. According to variousexamples, the stimulus location selection system includes a stimuluspresentation device 101. In particular examples, the stimuluspresentation device 101 is merely a display, monitor, screen, etc., thatdisplays stimulus material to a user. The stimulus material may be amedia clip, a commercial, pages of text, a brand image, a performance, amagazine advertisement, a movie, an experience, an audio presentation,and may even involve particular tastes, smells, textures and/or sounds.The stimuli can involve a variety of senses and occur with or withouthuman supervision. In other examples, stimulus presentation may involveproviding the user with a product, service, offering, or experience.Continuous and discrete modes are supported. According to variousexamples, the stimulus presentation device 101 also has protocolgeneration capability to allow intelligent customization of stimuliprovided to multiple subjects in different markets.

According to various examples, stimulus presentation device 101 couldinclude devices such as televisions, cable consoles, computers andmonitors, projection systems, display devices, speakers, tactilesurfaces, etc., for presenting the stimuli including but not limited toadvertising and entertainment from different networks, local networks,cable channels, syndicated sources, websites, internet contentaggregators, portals, service providers, etc.

According to various examples, the subjects are connected to datacollection devices 105. The data collection devices 105 may include avariety of neuro-response measurement mechanisms including neurologicaland neurophysiological measurements systems such as EEG, EOG, GSR, EKG,pupillary dilation, eye tracking, facial emotion encoding, and reactiontime devices, etc. According to various examples, neuro-response dataincludes central nervous system, autonomic nervous system, and effectordata. In particular examples, the data collection devices 105 includeEEG 111, EOG 113, and GSR 115. In some instances, only a single datacollection device is used. Data collection may proceed with or withouthuman supervision.

The data collection device 105 collects neuro-response data frommultiple sources. This includes a combination of devices such as centralnervous system sources (EEG), autonomic nervous system sources (GSR,EKG, pupillary dilation), and effector sources (EOG, eye tracking,facial emotion encoding, reaction time). In particular examples, datacollected is digitally sampled and stored for later analysis. Inparticular examples, the data collected could be analyzed in real-time.According to particular examples, the digital sampling rates areadaptively chosen based on the neurophysiological and neurological databeing measured.

In one particular example, the stimulus location selection systemincludes EEG 111 measurements made using scalp level electrodes, EOG 113measurements made using shielded electrodes to track eye data, GSR 115measurements performed using a differential measurement system, a facialmuscular measurement through shielded electrodes placed at specificlocations on the face, and a facial affect graphic and video analyzeradaptively derived for each individual.

In particular examples, the data collection devices are clocksynchronized with a stimulus presentation device 101. In particularexamples, the data collection devices 105 also include a conditionevaluation subsystem that provides auto triggers, alerts and statusmonitoring and visualization components that continuously monitor thestatus of the subject, data being collected, and the data collectioninstruments. The condition evaluation subsystem may also present visualalerts and automatically trigger remedial actions. According to variousexamples, the data collection devices include mechanisms for not onlymonitoring subject neuro-response to stimulus materials, but alsoinclude mechanisms for identifying and monitoring the stimulusmaterials. For example, data collection devices 105 may be synchronizedwith a set-top box to monitor channel changes. In other examples, datacollection devices 105 may be directionally synchronized to monitor whena subject is no longer paying attention to stimulus material. In stillother examples, the data collection devices 105 may receive and storestimulus material generally being viewed by the subject, whether thestimulus is a program, a commercial, printed material, an experience, ora scene outside a window. The data collected allows analysis ofneuro-response information and correlation of the information to actualstimulus material and not mere subject distractions.

According to various examples, the stimulus location selection systemalso includes a data cleanser device 121. In particular examples, thedata cleanser device 121 filters the collected data to remove noise,artifacts, and other irrelevant data using fixed and adaptive filtering,weighted averaging, advanced component extraction (like PCA, ICA),vector and component separation methods, etc. This device cleanses thedata by removing both exogenous noise (where the source is outside thephysiology of the subject, e.g. a phone ringing while a subject isviewing a video) and endogenous artifacts (where the source could beneurophysiological, e.g. muscle movements, eye blinks, etc.).

The artifact removal subsystem includes mechanisms to selectivelyisolate and review the response data and identify epochs with timedomain and/or frequency domain attributes that correspond to artifactssuch as line frequency, eye blinks, and muscle movements. The artifactremoval subsystem then cleanses the artifacts by either omitting theseepochs, or by replacing these epoch data with an estimate based on theother clean data (for example, an EEG nearest neighbor weightedaveraging approach).

According to various examples, the data cleanser device 121 isimplemented using hardware, firmware, and/or software. It should benoted that although a data cleanser device 121 is shown located after adata collection device 105 and before data analyzer 181, the datacleanser device 121 like other components may have a location andfunctionality that varies based on system implementation. For example,some systems may not use any automated data cleanser device whatsoeverwhile in other systems, data cleanser devices may be integrated intoindividual data collection devices.

According to various examples, an optional stimulus attributesrepository 131 provides information on the stimulus material beingpresented to the multiple subjects. According to various examples,stimulus attributes include properties of the stimulus materials as wellas purposes, presentation attributes, report generation attributes, etc.In particular examples, stimulus attributes include time span, channel,rating, media, type, etc. Stimulus attributes may also include positionsof entities in various frames, components, events, object relationships,locations of objects and duration of display. Purpose attributes includeaspiration and objects of the stimulus including excitement, memoryretention, associations, etc. Presentation attributes include audio,video, imagery, and messages needed for enhancement or avoidance. Otherattributes may or may not also be included in the stimulus attributesrepository or some other repository.

The data cleanser device 121 and the stimulus attributes repository 131pass data to the data analyzer 181. The data analyzer 181 uses a varietyof mechanisms to analyze underlying data in the system to placestimulus. According to various examples, the data analyzer customizesand extracts the independent neurological and neuro-physiologicalparameters for each individual in each modality, and blends theestimates within a modality as well as across modalities to elicit anenhanced response to the presented stimulus material. In particularexamples, the data analyzer 181 aggregates the response measures acrosssubjects in a dataset.

According to various examples, neurological and neuro-physiologicalsignatures are measured using time domain analyses and frequency domainanalyses. Such analyses use parameters that are common acrossindividuals as well as parameters that are unique to each individual.The analyses could also include statistical parameter extraction andfuzzy logic based attribute estimation from both the time and frequencycomponents of the synthesized response.

In some examples, statistical parameters used in a blended effectivenessestimate include evaluations of skew, peaks, first and second moments,population distribution, as well as fuzzy estimates of attention,emotional engagement and memory retention responses.

According to various examples, the data analyzer 181 may include anintra-modality response synthesizer and a cross-modality responsesynthesizer. In particular examples, the intra-modality responsesynthesizer is configured to customize and extract the independentneurological and neurophysiological parameters for each individual ineach modality and blend the estimates within a modality analytically toelicit an enhanced response to the presented stimuli. In particularexamples, the intra-modality response synthesizer also aggregates datafrom different subjects in a dataset.

According to various examples, the cross-modality response synthesizeror fusion device blends different intra-modality responses, includingraw signals and signals output. The combination of signals enhances themeasures of effectiveness within a modality. The cross-modality responsefusion device can also aggregate data from different subjects in adataset.

According to various examples, the data analyzer 181 also includes acomposite enhanced effectiveness estimator (CEEE) that combines theenhanced responses and estimates from each modality to provide a blendedestimate of the effectiveness. In particular examples, blended estimatesare provided for each exposure of a subject to stimulus materials. Theblended estimates are evaluated over time to assess stimulus locationcharacteristics. According to various examples, numerical values areassigned to each blended estimate. The numerical values may correspondto the intensity of neuro-response measurements, the significance ofpeaks, the change between peaks, etc. Higher numerical values maycorrespond to higher significance in neuro-response intensity. Lowernumerical values may correspond to lower significance or eveninsignificant neuro-response activity. In other examples, multiplevalues are assigned to each blended estimate. In still other examples,blended estimates of neuro-response significance are graphicallyrepresented to show changes after repeated exposure.

According to various examples, the data analyzer 181 provides analyzedand enhanced response data to a data communication device 183. It shouldbe noted that in particular instances, a data communication device 183is not necessary. According to various examples, the data communicationdevice 183 provides raw and/or analyzed data and insights. In particularexamples, the data communication device 183 may include mechanisms forthe compression and encryption of data for secure storage andcommunication.

According to various examples, the data communication device 183transmits data using protocols such as the File Transfer Protocol (FTP),Hypertext Transfer Protocol (HTTP) along with a variety of conventional,bus, wired network, wireless network, satellite, and proprietarycommunication protocols. The data transmitted can include the data inits entirety, excerpts of data, converted data, and/or elicited responsemeasures. According to various examples, the data communication deviceis a set top box, wireless device, computer system, etc. that transmitsdata obtained from a data collection device to a response integrationsystem 185. In particular examples, the data communication device maytransmit data even before data cleansing or data analysis. In otherexamples, the data communication device may transmit data after datacleansing and analysis.

In particular examples, the data communication device 183 sends data toa response integration system 185. According to various examples, theresponse integration system 185 assesses and extracts stimulus placementcharacteristics. In particular examples, the response integration system185 determines entity positions in various stimulus segments and matchesposition information with eye tracking paths while correlating saccadeswith neural assessments of attention, memory retention, and emotionalengagement. In particular examples, the response integration system 185also collects and integrates user behavioral and survey responses withthe analyzed response data to more effectively select stimuluslocations.

A variety of data can be stored for later analysis, management,manipulation, and retrieval. In particular examples, the repositorycould be used for tracking stimulus attributes and presentationattributes, audience responses and optionally could also be used tointegrate audience measurement information.

As with a variety of the components in the system, the responseintegration system can be co-located with the rest of the system and theuser, or could be implemented in a remote location. It could also beoptionally separated into an assessment repository system that could becentralized or distributed at the provider or providers of the stimulusmaterial. In other examples, the response integration system is housedat the facilities of a third party service provider accessible bystimulus material providers and/or users.

FIG. 2 illustrates examples of data models that may be provided with astimulus attributes repository. According to various examples, astimulus attributes data model 201 includes a channel 203, media type205, time span 207, audience 209, and demographic information 211. Astimulus purpose data model 215 may include intents 217 and objectives219. According to various examples, stimulus attributes data model 201also includes candidate location information 221 about various temporal,spatial, activity, and event components in an experience that may holdstimulus material. For example, a movie may show a blank wall includedon some scenes that can be used to display an advertisement. Thetemporal and spatial characteristics of the blank wall may be providedin candidate location information 221.

According to various examples, another stimulus attributes data modelincludes creation attributes 223, ownership attributes 225, broadcastattributes 227, and statistical, demographic and/or survey basedidentifiers for automatically integrating the neuro-physiological andneuro-behavioral response with other attributes and meta-informationassociated with the stimulus.

FIG. 3 illustrates examples of data models that can be used for storageof information associated with selection of locations for theintroduction of stimulus material. According to various examples, adataset data model 301 includes an experiment name 303 and/oridentifier, client attributes 305, a subject pool 307, logisticsinformation 309 such as the location, date, and time of testing, andstimulus material 311 including stimulus material attributes.

In particular examples, a subject attribute data model 315 includes asubject name 317 and/or identifier, contact information 321, anddemographic attributes 319 that may be useful for review of neurologicaland neuro-physiological data. Some examples of pertinent demographicattributes include marriage status, employment status, occupation,household income, household size and composition, ethnicity, geographiclocation, sex, race. Other fields that may be included in data model 315include shopping preferences, entertainment preferences, and financialpreferences. Shopping preferences include favorite stores, shoppingfrequency, categories shopped, favorite brands. Entertainmentpreferences include network/cable/satellite access capabilities,favorite shows, favorite genres, and favorite actors. Financialpreferences include favorite insurance companies, preferred investmentpractices, banking preferences, and favorite online financialinstruments. A variety of subject attributes may be included in asubject attributes data model 315 and data models may be preset orcustom generated to suit particular purposes.

According to various examples, data models for neuro-feedbackassociation 325 identify experimental protocols 327, modalities included329 such as EEG, EOG, GSR, surveys conducted, and experiment designparameters 333 such as segments and segment attributes. Other fields mayinclude experiment presentation scripts, segment length, segment detailslike stimulus material used, inter-subject variations, intra-subjectvariations, instructions, presentation order, survey questions used,etc. Other data models may include a data collection data model 337.According to various examples, the data collection data model 337includes recording attributes 339 such as station and locationidentifiers, the data and time of recording, and operator details. Inparticular examples, equipment attributes 341 include an amplifieridentifier and a sensor identifier.

Modalities recorded 343 may include modality specific attributes likeEEG cap layout, active channels, sampling frequency, and filters used.EOG specific attributes include the number and type of sensors used,location of sensors applied, etc. Eye tracking specific attributesinclude the type of tracker used, data recording frequency, data beingrecorded, recording format, etc. According to various examples, datastorage attributes 345 include file storage conventions (format, namingconvention, dating convention), storage location, archival attributes,expiry attributes, etc.

A preset query data model 349 includes a query name 351 and/oridentifier, an accessed data collection 353 such as data segmentsinvolved (models, databases/cubes, tables, etc.), access securityattributes 355 included who has what type of access, and refreshattributes 357 such as the expiry of the query, refresh frequency, etc.Other fields such as push-pull preferences can also be included toidentify an auto push reporting driver or a user driven report retrievalsystem.

FIG. 4 illustrates examples of queries that can be performed to obtaindata associated with stimulus location selection. For example, users mayquery to determine what types of consumers respond most to a particularexperience or component of an experience. According to various examples,queries are defined from general or customized scripting languages andconstructs, visual mechanisms, a library of preset queries, diagnosticquerying including drill-down diagnostics, and eliciting what ifscenarios. According to various examples, subject attributes queries 415may be configured to obtain data from a neuro-informatics repositoryusing a location 417 or geographic information, session information 421such as testing times and dates, and demographic attributes 419.Demographics attributes include household income, household size andstatus, education level, age of kids, etc.

Other queries may retrieve stimulus material based on shoppingpreferences of subject participants, countenance, physiologicalassessment, completion status. For example, a user may query for dataassociated with product categories, products shopped, shops frequented,subject eye correction status, color blindness, subject state, signalstrength of measured responses, alpha frequency band ringers, musclemovement assessments, segments completed, etc. Experimental design basedqueries may obtain data from a neuro-informatics repository based onexperiment protocols 427, product category 429, surveys included 431,and stimulus provided 433. Other fields that may be used include thenumber of protocol repetitions used, combination of protocols used, andusage configuration of surveys.

Client and industry based queries may obtain data based on the types ofindustries included in testing, specific categories tested, clientcompanies involved, and brands being tested. Response assessment basedqueries 437 may include attention scores 439, emotion scores, 441,retention scores 443, and effectiveness scores 445. Such queries mayobtain materials that elicited particular scores.

Response measure profile based queries may use mean measure thresholds,variance measures, number of peaks detected, etc. Group response queriesmay include group statistics like mean, variance, kurtosis, p-value,etc., group size, and outlier assessment measures. Still other queriesmay involve testing attributes like test location, time period, testrepetition count, test station, and test operator fields. A variety oftypes and combinations of types of queries can be used to efficientlyextract data.

FIG. 5 illustrates examples of reports that can be generated. Accordingto various examples, client assessment summary reports 501 includeeffectiveness measures 503, component assessment measures 505, andstimulus location effectiveness measures 507. Effectiveness assessmentmeasures include composite assessment measure(s),industry/category/client specific placement (percentile, ranking, etc.),actionable grouping assessment such as removing material, modifyingsegments, or fine tuning specific elements, etc, and the evolution ofthe effectiveness profile over time. In particular examples, componentassessment reports include component assessment measures like attention,emotional engagement scores, percentile placement, ranking, etc.Component profile measures include time based evolution of the componentmeasures and profile statistical assessments. According to variousexamples, reports include the number of times material is assessed,attributes of the multiple presentations used, evolution of the responseassessment measures over the multiple presentations, and usagerecommendations.

According to various examples, client cumulative reports 511 includemedia grouped reporting 513 of all stimulus assessed, campaign groupedreporting 515 of stimulus assessed, and time/location grouped reporting517 of stimulus assessed. According to various examples, industrycumulative and syndicated reports 521 include aggregate assessmentresponses measures 523, top performer lists 525, bottom performer lists527, outliers 529, and trend reporting 531. In particular examples,tracking and reporting includes specific products, categories,companies, brands.

FIG. 6 illustrates one example of stimulus location selection. At 601,stimulus material is provided to multiple subjects in multiplegeographic markets. According to various examples, stimulus includesstreaming video and audio provided over mechanisms such as broadcasttelevision, cable television, satellite, etc. Alternatively, stimulusmay involve actual physical products, services, interactions, andexperiences. At 603, subject responses are collected using a variety ofmodalities, such as EEG, ERP, EOG, GSR, etc. In some examples, verbaland written responses can also be collected and correlated withneurological and neurophysiological responses. At 605, data is passedthrough a data cleanser to remove noise and artifacts that may make datamore difficult to interpret. According to various examples, the datacleanser removes EEG electrical activity associated with blinking andother endogenous/exogenous artifacts.

According to various examples, data analysis is performed. Data analysismay include intra-modality response synthesis and cross-modalityresponse synthesis to enhance effectiveness measures. It should be notedthat in some particular instances, one type of synthesis may beperformed without performing other types of synthesis. For example,cross-modality response synthesis may be performed with or withoutintra-modality synthesis.

A variety of mechanisms can be used to perform data analysis. Inparticular examples, a stimulus attributes repository is accessed toobtain attributes and characteristics of the stimulus materials, alongwith purposes, intents, objectives, etc. In particular examples, EEGresponse data is synthesized to provide an enhanced assessment ofeffectiveness. According to various examples, EEG measures electricalactivity resulting from thousands of simultaneous neural processesassociated with different portions of the brain. EEG data can beclassified in various bands. According to various examples, brainwavefrequencies include delta, theta, alpha, beta, and gamma frequencyranges. Delta waves are classified as those less than 4 Hz and areprominent during deep sleep. Theta waves have frequencies between 3.5 to7.5 Hz and are associated with memories, attention, emotions, andsensations. Theta waves are typically prominent during states ofinternal focus.

Alpha frequencies reside between 7.5 and 13 Hz and typically peak around10 Hz. Alpha waves are prominent during states of relaxation. Beta waveshave a frequency range between 14 and 30 Hz. Beta waves are prominentduring states of motor control, long range synchronization between brainareas, analytical problem solving, judgment, and decision making. Gammawaves occur between 30 and 60 Hz and are involved in binding ofdifferent populations of neurons together into a network for the purposeof carrying out a certain cognitive or motor function, as well as inattention and memory. Because the skull and dermal layers attenuatewaves in this frequency range, brain waves above 75-80 Hz are difficultto detect and are often not used for stimuli response assessment.

However, the techniques and mechanisms of the present disclosurerecognize that analyzing high gamma band (kappa-band: Above 60 Hz)measurements, in addition to theta, alpha, beta, and low gamma bandmeasurements, enhances neurological attention, emotional engagement andretention component estimates. In particular examples, EEG measurementsincluding difficult to detect high gamma or kappa band measurements areobtained, enhanced, and evaluated. Subject and task specific signaturesub-bands in the theta, alpha, beta, gamma and kappa bands areidentified to provide enhanced response estimates. According to variousexamples, high gamma waves (kappa-band) above 80 Hz (typicallydetectable with sub-cranial EEG and/or magnetoencephalography) can beused in inverse model-based enhancement of the frequency responses tothe stimuli.

Various examples of the present disclosure recognize that particularsub-bands within each frequency range have particular prominence duringcertain activities. A subset of the frequencies in a particular band isreferred to herein as a sub-band. For example, a sub-band may includethe 40-45 Hz range within the gamma band. In particular examples,multiple sub-bands within the different bands are selected whileremaining frequencies are band pass filtered. In particular examples,multiple sub-band responses may be enhanced, while the remainingfrequency responses may be attenuated.

An information theory based band-weighting model is used for adaptiveextraction of selective dataset specific, subject specific, taskspecific bands to enhance the effectiveness measure. Adaptive extractionmay be performed using fuzzy scaling. Stimuli can be presented andenhanced measurements determined multiple times to determine thevariation profiles across multiple presentations. Determining variousprofiles provides an enhanced assessment of the primary responses aswell as the longevity (wear-out) of the marketing and entertainmentstimuli. The synchronous response of multiple individuals to stimulipresented in concert is measured to determine an enhanced across subjectsynchrony measure of effectiveness. According to various examples, thesynchronous response may be determined for multiple subjects residing inseparate locations or for multiple subjects residing in the samelocation.

Although a variety of synthesis mechanisms are described, it should berecognized that any number of mechanisms can be applied—in sequence orin parallel with or without interaction between the mechanisms.

Although intra-modality synthesis mechanisms provide enhancedsignificance data, additional cross-modality synthesis mechanisms canalso be applied. A variety of mechanisms such as EEG, Eye Tracking, GSR,EOG, and facial emotion encoding are connected to a cross-modalitysynthesis mechanism. Other mechanisms as well as variations andenhancements on existing mechanisms may also be included. According tovarious examples, data from a specific modality can be enhanced usingdata from one or more other modalities. In particular examples, EEGtypically makes frequency measurements in different bands like alpha,beta and gamma to provide estimates of significance. However, thetechniques of the present disclosure recognize that significancemeasures can be enhanced further using information from othermodalities.

For example, facial emotion encoding measures can be used to enhance thevalence of the EEG emotional engagement measure. EOG and eye trackingsaccadic measures of object entities can be used to enhance the EEGestimates of significance including but not limited to attention,emotional engagement, and memory retention. According to variousexamples, a cross-modality synthesis mechanism performs time and phaseshifting of data to allow data from different modalities to align. Insome examples, it is recognized that an EEG response will often occurhundreds of milliseconds before a facial emotion measurement changes.Correlations can be drawn and time and phase shifts made on anindividual as well as a group basis. In other examples, saccadic eyemovements may be determined as occurring before and after particular EEGresponses. According to various examples, time corrected GSR measuresare used to scale and enhance the EEG estimates of significanceincluding attention, emotional engagement and memory retention measures.

Evidence of the occurrence or non-occurrence of specific time domaindifference event-related potential components (like the DERP) inspecific regions correlates with subject responsiveness to specificstimulus. According to various examples, ERP measures are enhanced usingEEG time-frequency measures (ERPSP) in response to the presentation ofthe marketing and entertainment stimuli. Specific portions are extractedand isolated to identify ERP, DERP and ERPSP analyses to perform. Inparticular examples, an EEG frequency estimation of attention, emotionand memory retention (ERPSP) is used as a co-factor in enhancing theERP, DERP and time-domain response analysis.

EOG measures saccades to determine the presence of attention to specificobjects of stimulus. Eye tracking measures the subject's gaze path,location and dwell on specific objects of stimulus. According to variousexamples, EOG and eye tracking is enhanced by measuring the presence oflambda waves (a neurophysiological index of saccade effectiveness) inthe ongoing EEG in the occipital and extra striate regions, triggered bythe slope of saccade-onset to estimate the significance of the EOG andeye tracking measures. In particular examples, specific EEG signaturesof activity such as slow potential shifts and measures of coherence intime-frequency responses at the Frontal Eye Field (FEF) regions thatpreceded saccade-onset are measured to enhance the effectiveness of thesaccadic activity data.

GSR typically measures the change in general arousal in response tostimulus presented. According to various examples, GSR is enhanced bycorrelating EEG/ERP responses and the GSR measurement to get an enhancedestimate of subject engagement. The GSR latency baselines are used inconstructing a time-corrected GSR response to the stimulus. Thetime-corrected GSR response is co-factored with the EEG measures toenhance GSR significance measures.

According to various examples, facial emotion encoding uses templatesgenerated by measuring facial muscle positions and movements ofindividuals expressing various emotions prior to the testing session.These individual specific facial emotion encoding templates are matchedwith the individual responses to identify subject emotional response. Inparticular examples, these facial emotion encoding measurements areenhanced by evaluating inter-hemispherical asymmetries in EEG responsesin specific frequency bands and measuring frequency band interactions.The techniques of the present disclosure recognize that not only areparticular frequency bands significant in EEG responses, but particularfrequency bands used for communication between particular areas of thebrain are significant. Consequently, these EEG responses enhance theEMG, graphic and video based facial emotion identification.

According to various examples, post-stimulus versus pre-stimulusdifferential measurements of ERP time domain components in multipleregions of the brain (DERP) are measured at 607. The differentialmeasures give a mechanism for eliciting responses attributable to thestimulus. For example the messaging response attributable to an ad orthe brand response attributable to multiple brands is determined usingpre-experience and post-experience estimates

At 609, target versus distracter stimulus differential responses aredetermined for different regions of the brain (DERP). At 613, eventrelated time-frequency analysis of the differential response (DERPSPs)are used to assess the attention, emotion and memory retention measuresacross multiple frequency bands. According to various examples, themultiple frequency bands include theta, alpha, beta, gamma and highgamma or kappa.

At 615, neuro-response lulls in source material are identified. Forexample, there may be locations in a particular media stream that elicitminimal neuro-response measurements. These lulls may last severalseconds in duration in a commercial or may cover several feet of spaceon a supermarket shelf. These locations with insignificantneuro-response activity may be selected a potential locations where newstimulus material may be introduced. For example, brand images,messages, video clips, newly packaged products, etc. may all be part ofthe stimulus material selected for introduction into source material.Although lulls may be used to select candidate locations for theintroduction of stimulus material, it should be noted that a variety ofmechanisms may be used. In some examples, non-changing portions such asstill screens in a video or blank or common colored areas in an imagemay be selected as candidate locations. Locations having little changein relation to neighboring locations may also be selected. In stillother examples, locations are manually selected. At 623, multiple trialsare performed with introduced stimulus material in different spatial andtemporal locations to assess the impact of introduction at each of thedifferent spatial and temporal locations.

For example, introduction of new products at location A on a billboardmay lead to more significant neuro-response activity for the billboardin general. Introduction of an image onto a video stream may lead togreater emotional engagement and memory retention. In other examples,increased neuro-response activity for introduced material may detractfrom neuro-response activity for other portions of source material. Forexamples, a salient image on one part of a billboard may lead to reduceddwell times for other portions of a billboard. Acve, aggregatedneuro-response measurements are identified to determine optimallocations for introduction of stimulus material.

At 625, processed data is provided to a data communication device fortransmission over a network such as a wireless, wireline, satellite, orother type of communication network capable of transmitting data. Datais provided to response integration system at 627. According to variousexamples, the data communication device transmits data using protocolssuch as the File Transfer Protocol (FTP), Hypertext Transfer Protocol(HTTP) along with a variety of conventional, bus, wired network,wireless network, satellite, and proprietary communication protocols.The data transmitted can include the data in its entirety, excerpts ofdata, converted data, and/or elicited response measures. According tovarious examples, data is sent using a telecommunications, wireless,Internet, satellite, or any other communication mechanisms that iscapable of conveying information from multiple subject locations fordata integration and analysis. The mechanism may be integrated in a settop box, computer system, receiver, mobile device, etc.

In particular examples, the data communication device sends data to theresponse integration system 627. According to various examples, theresponse integration system 627 combines the analyzed responses to theexperience/stimuli, with information on the available stimuli and itsattributes. A variety of responses including user behavioral and surveyresponses are also collected an integrated. At 629, a location isselected for the introduction of stimulus material.

According to various examples, the response integration system combinesanalyzed and enhanced responses to the stimulus material while usinginformation about stimulus material attributes such as the location,movement, acceleration, and spatial relationships of various entitiesand objects. In particular examples, the response integration systemalso collects and integrates user behavioral and survey responses withthe analyzed and enhanced response data to more effectively assessstimulus location characteristics.

According to various examples, the stimulus location selection systemprovides data to a repository for the collection and storage ofdemographic, statistical and/or survey based responses to differententertainment, marketing, advertising and otheraudio/visual/tactile/olfactory material. If this information is storedexternally, this system could include a mechanism for the push and/orpull integration of the data—including but not limited to querying,extracting, recording, modifying, and/or updating. This systemintegrates the requirements for the presented material, the assessedneuro-physiological and neuro-behavioral response measures, and theadditional stimulus attributes such as demography/statistical/surveybased responses into a synthesized measure for the selection of stimuluslocations.

According to various examples, the repository stores information fortemporal, spatial, activity, and event based components of stimulusmaterial. For example, neuro-response data, statistical data, surveybased response data, and demographic data may be aggregated and storedand associated with a particular component in a video stream.

FIG. 7 illustrates an example of a technique for selecting stimuluslocations experience. According to various examples, measurements fromdifferent modalities are obtained at 701. According to various examples,measurements including EEG, GSR, EOG, EKG, DERP, DERPSP, PupilaryResponse, etc., are blended to obtain a combined measurement at 703. Inparticular examples, each measurement may have to be alignedappropriately in order to allow blending. According to various examples,a response integration system includes mechanisms to use and blenddifferent measures from across the modalities from the data analyzer. Inparticular examples, the data includes the EEG, GSR, EOG, EKG, DERP,DERPSPs, pupilary response, GSR, eye movement, coherence, coupling andlambda wave based response. Measurements across modalities are blendedto select one or more stimulus locations.

At 705, neuro-response measurements are combined with statistical,demographic, and/or survey based information. At 707, candidatelocations for the introduction of stimulus material are identified.Candidate locations may include lulls, locations with less significantor insignificant neuro-response activity, non-changing scenes, ormanually selected positions. In still other examples, candidatelocations are identified by a stimulus material provider. A variety ofmechanisms can be used to identify candidate locations. At 709,neuro-response significance is evaluated for the source material whenstimulus material is introduced at the various candidate locations.Evaluation may include memory retention, emotional engagement, attentionof source material are locations where stimulus material is introducedor in the aggregate. In some examples, differential measurements of ERPtime domain components at multiple regions of the brain are determined(DERP). Event related time-frequency analysis of the differentialresponse is performed to assess the attention, emotion and memoryretention (DERPSPs) across multiple frequency bands including but notlimited to theta, alpha, beta, gamma and high gamma In particularexamples, single trial and/or averaged DERP and/or DERPSPs can be usedto further compare locations for the introduction of stimulus material.

At 711, preferred locations in the source material are selected for theintroduction of stimulus material.

The location and placement assessment system can further include anadaptive learning component that refines profiles and tracks variationsresponses to particular stimuli or series of stimuli over time.

According to various examples, various mechanisms such as the datacollection mechanisms, the intra-modality synthesis mechanisms,cross-modality synthesis mechanisms, etc. are implemented on multipledevices. However, it is also possible that the various mechanisms beimplemented in hardware, firmware, and/or software in a single system.FIG. 8 provides one example of a system that can be used to implementone or more mechanisms. For example, the system shown in FIG. 8 may beused to implement a stimulus location selection system.

According to particular examples, a system 800 suitable for implementingparticular examples of the present disclosure includes a processor 801,a memory 803, an interface 811, and a bus 815 (e.g., a PCI bus). Whenacting under the control of appropriate software or firmware, theprocessor 801 is responsible for such tasks such as pattern generation.Various specially configured devices can also be used in place of aprocessor 801 or in addition to processor 801. The completeimplementation can also be done in custom hardware. The interface 811 istypically configured to send and receive data packets or data segmentsover a network. Particular examples of interfaces the device supportsinclude host bus adapter (HBA) interfaces, Ethernet interfaces, framerelay interfaces, cable interfaces, DSL interfaces, token ringinterfaces, and the like.

In addition, various high-speed interfaces may be provided such as fastEthernet interfaces, Gigabit Ethernet interfaces, ATM interfaces, HSSIinterfaces, POS interfaces, FDDI interfaces and the like. Generally,these interfaces may include ports appropriate for communication withthe appropriate media. In some cases, they may also include anindependent processor and, in some instances, volatile RAM. Theindependent processors may control such communications intensive tasksas data synthesis.

According to particular examples, the system 800 uses memory 803 tostore data, algorithms and program instructions. The programinstructions may control the operation of an operating system and/or oneor more applications, for example. The memory or memories may also beconfigured to store received data and process received data.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present disclosurerelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include, but arenot limited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks and DVDs;magneto-optical media such as optical disks; and hardware devices thatare specially configured to store and perform program instructions, suchas read-only memory devices (ROM) and random access memory (RAM).Examples of program instructions include both machine code, such asproduced by a compiler, and files containing higher level code that maybe executed by the computer using an interpreter.

Although the foregoing disclosure has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. Therefore, the present examples are to be considered asillustrative and not restrictive and the disclosure is not to be limitedto the details given herein, but may be modified within the scope andequivalents of the appended claims.

1. (canceled)
 2. A method, comprising: analyzing first neuro-responsedata from a first subject exposed to source material; identifying acandidate location in the source material for introduction of anadvertisement or entertainment based on the first neuro-response data;analyzing second neuro-response data from at least one of the firstsubject and a second subject exposed to a combination of the sourcematerial and the advertisement or entertainment inserted in thecandidate location; and determining an effectiveness of theadvertisement or entertainment based on the second neuro-response data.3. The method of claim 2, wherein the neuro-response data comprises afirst frequency band of electroencephalographic data and a secondfrequency band of electroencephalographic data, the second frequencyband being different than the first frequency band, and whereinidentifying the candidate location is based on an interaction betweenthe first frequency band and the second frequency band.
 4. The method ofclaim 3, wherein the interaction comprises a degree of coherence betweena first change in amplitude of the first frequency band and a secondchange in amplitude of the second frequency band.
 5. The method of claim2, wherein the first neuro-response data comprises firstelectroencephalographic data from a first region of a brain of the firstsubject and second electroencephalographic data from a second region ofthe brain, and wherein identifying the candidate location is based on aninteraction between the first electroencephalographic data and thesecond electroencephalographic data.
 6. The method of claim 5, whereinthe interaction comprises a degree of coherence between a first changein amplitude of the first electroencephalographic data and a secondchange in amplitude of the second electroencephalographic data.
 7. Themethod of claim 2, wherein identifying the candidate location is furtherbased on a visual characteristic of the candidate location.
 8. Themethod of claim 2, wherein identifying the candidate location is furtherbased on a temporal characteristic of the candidate location.
 9. Themethod of claim 2 further comprising: assigning a first numerical valueto a first portion of the first neuro-response data gathered from afirst portion of the source material, the first neuro-response datacomprising first electroencephalographic data having a first frequencyand the first numerical value based on at least one of a first amplitudeof the first frequency or a first change in the first amplitude of thefirst frequency; assigning a second numerical value to a second portionof the first neuro-response data gathered from a second portion of thesource material, the first neuro-response data comprising secondelectroencephalographic data having a second frequency and the secondnumerical value based on at least one of a second amplitude of thesecond frequency or a second change in the second amplitude of thesecond frequency; determining which of the first numerical value or thesecond number value is the lower numerical value; identifying which ofthe first portion of the first neuro-response data or the second portionof the first neuro-response data corresponds to the lower numericalvalue; and identifying the candidate location as the one of the firstportion of the source material or the second portion of the sourcematerial corresponding to the first portion of the first neuro-responsedata or the second portion of the first neuro-response data having thelower numerical value.
 10. The method of claim 9, wherein the firstfrequency and the second frequency are a same frequency gathered atdifferent times.
 11. A system, comprising: an analyzer to analyze firstneuro-response data from a first subject exposed to source material; anda processor to identifying a candidate location in the source materialfor introduction of an advertisement or entertainment based on the firstneuro-response data, the analyzer to analyze second neuro-response datafrom at least one of the first subject and a second subject exposed to acombination of the source material and the advertisement orentertainment inserted in the candidate location, and the processor todetermine an effectiveness of the advertisement or entertainment basedon the second neuro-response data.
 12. The system of claim 11, whereinthe neuro-response data comprises a first frequency band ofelectroencephalographic data and a second frequency band ofelectroencephalographic data, the second frequency band being differentthan the first frequency band, and the processor to identify thecandidate location based on a degree of coherence between a first changein amplitude of the first frequency band and a second change inamplitude of the second frequency band.
 13. The system of claim 11,wherein the first neuro-response data comprises firstelectroencephalographic data from a first region of a brain of the firstsubject and second electroencephalographic data from a second region ofthe brain, and the processor is to identify the candidate location basedon a degree of coherence between a first change in amplitude of thefirst electroencephalographic data and a second change in amplitude ofthe second electroencephalographic data.
 14. The system of claim 11,wherein the processor is to identify the candidate location based on avisual characteristic of the candidate location.
 15. The system of claim11, wherein the processor is to identify the candidate location based ona temporal characteristic of the candidate location.
 16. The system ofclaim 11, wherein the processor is to: assign a first numerical value toa first portion of the first neuro-response data gathered from a firstportion of the source material, the first neuro-response data comprisingfirst electroencephalographic data having a first frequency and thefirst numerical value based on at least one of a first amplitude of thefirst frequency or a first change in the first amplitude of the firstfrequency; assign a second numerical value to a second portion of thefirst neuro-response data gathered from a second portion of the sourcematerial, the first neuro-response data comprising secondelectroencephalographic data having a second frequency and the secondnumerical value based on at least one of a second amplitude of thesecond frequency or a second change in the second amplitude of thesecond frequency; determine which of the first numerical value or thesecond number value is the lower numerical value; identify which of thefirst portion of the first neuro-response data or the second portion ofthe first neuro-response data corresponds to the lower numerical value;and identify the candidate location as the one of the first portion ofthe source material or the second portion of the source materialcorresponding to the first portion of the first neuro-response data orthe second portion of the first neuro-response data having the lowernumerical value.
 17. The system of claim 16, wherein the first frequencyand the second frequency are a same frequency gathered at differenttimes.
 18. A tangible machine readable storage device or storage disccomprising machine readable instructions which, when read, cause amachine to at least: analyze first neuro-response data from a firstsubject exposed to source material; and identifying a candidate locationin the source material for introduction of an advertisement orentertainment based on the first neuro-response data; analyze secondneuro-response data from at least one of the first subject and a secondsubject exposed to a combination of the source material and theadvertisement or entertainment inserted in the candidate location; anddetermine an effectiveness of the advertisement or entertainment basedon the second neuro-response data.
 19. The machine readable device ordisc of claim 18, wherein the neuro-response data comprises a firstfrequency band of electroencephalographic data and a second frequencyband of electroencephalographic data, the second frequency band beingdifferent than the first frequency band, and wherein the instructionscause the machine to identify the candidate location based on a degreeof coherence between a first change in amplitude of the first frequencyband and a second change in amplitude of the second frequency band. 20.The machine readable device or disc of claim 18, wherein the firstneuro-response data comprises first electroencephalographic data from afirst region of a brain of the first subject and secondelectroencephalographic data from a second region of the brain, and theinstructions cause the machine to identify the candidate location basedon a degree of coherence between a first change in amplitude of thefirst electroencephalographic data and a second change in amplitude ofthe second electroencephalographic data.
 21. The machine readable deviceor disc of claim 18, wherein the instructions cause the machine to:assign a first numerical value to a first portion of the firstneuro-response data gathered from a first portion of the sourcematerial, the first neuro-response data comprising firstelectroencephalographic data having a first frequency and the firstnumerical value based on at least one of a first amplitude of the firstfrequency or a first change in the first amplitude of the firstfrequency; assign a second numerical value to a second portion of thefirst neuro-response data gathered from a second portion of the sourcematerial, the first neuro-response data comprising secondelectroencephalographic data having a second frequency and the secondnumerical value based on at least one of a second amplitude of thesecond frequency or a second change in the second amplitude of thesecond frequency; determine which of the first numerical value or thesecond number value is the lower numerical value; identify which of thefirst portion of the first neuro-response data or the second portion ofthe first neuro-response data corresponds to the lower numerical value;and identify the candidate location as the one of the first portion ofthe source material or the second portion of the source materialcorresponding to the first portion of the first neuro-response data orthe second portion of the first neuro-response data having the lowernumerical value.